System and method for strategizing interactions with a client base

ABSTRACT

The present disclosure describes novel systems and methods that can be utilized to evaluate and/or direct an interaction with a consumer database and/or evaluate a consumer database, where the consumer database contains information about consumers and particular products and/or services held or used by the consumers. The interactions may be, for example, determining a strategy for sales, marketing, cross-selling, and/or retaining one or more of the consumers. The evaluations may include, for example, hierarchically ranking the consumers and/or determining a clustering of the consumers.

RELATED APPLICATIONS

The present application claims priority benefit of U.S. application Ser.No. 13/187,750 filed 21 Jul. 2011 entitled “System and Method forStrategizing Interactions With a Client Base” which itself claimspriority benefit of U.S. Provisional Application No. 61/366,402, filed21 Jul. 2010 entitled “System and Method for Analyzing Client BaseValue”, each of which is hereby incorporated herein by reference.

BACKGROUND

Most organizations, in particular large organizations with a significantclient/consumer base, are constantly faced with the problem ofmaximizing the value they can extract from their clients/consumers whileminimizing the cost in doing so. Preferably, the organization would liketo institute one or more programs that are both effective at targetingthe clients' needs while efficiently applying a limited amount ofresources (e.g., money, time, and effort). However, typical client basesare made up of disparate individuals/entities that have widely varyingbehaviors, attitudes, and needs and may further have varyingpropensities for obtaining new products and/or services from theorganization.

Typically, most prior art systems and methods address the issue of howto model the needs/desires of their client/consumer base from aproduct-oriented approach rather than a client-oriented approach. Forexample, traditional sales models focus on selling a particular productand then determine which clients, or an approximation of a “typicalclient”, are most likely to purchase that particular product. Such anapproach directs interactions with the clients towards a sales call ormarketing effort that is focused on the particular product first andglosses over the attitudes, desires, and needs of the clients. Theseprior art approaches typically results in relatively low sales rates(e.g., 2% direct mail response) since there is no real effort made bythe organization to determine what the client wants.

Some prior art approaches may attempt to take the client's needs intoaccount, but still these approaches retain the focus on what theorganization wants, i.e., the product to be sold. One reason for this isthat even the most robust client and product database has limitedinformation about the client and this dearth of client information makesit difficult, if not impossible, to distinguish clients who may appearto be similar but in actuality have widely varying attitudes, behaviors,and needs.

At best, prior art systems and methods cannot achieve the necessarygranularity of analysis in order to direct how the organization shouldinteract with the various needs and concerns of its clients.Consequently, prior art systems and methods may only target avaguely-determined “median person” or “median behavior” or “the averageJoe” or some other gross characterization of the client base. In someinstances, prior art systems and methods may target a very limitednumber of groups, e.g., “the average Joe” and “the average Jane”, andperhaps “the average Jim”. Such prior art systems and methods operateunder the assumption that “the average Joe/Jane/Jim” is a sufficientindicator of how most of the client base will act and the organizationwill conduct itself accordingly when interacting with eachclient/consumer in the client base. Obviously, lumping everyone in onebasket in a “one size fits all” approach or even into a few baskets isnot an effective means of dealing with a client/consumer base made up ofdistinct, and perhaps contrasting, individuals and behaviors.

For example, prior art systems and methods which direct how anorganization should interact with its clients using “the average Joe”approach are most likely to determine a single sales strategy and/ormarketing strategy and/or cross-selling strategy, and/or consumerretention strategy and apply that single strategy to every consumerregardless of whether a particular consumer is even interested in, e.g.,new sales, or a cross-sell product or service. Additionally, “theaverage Joe” approach may result in a uniformly-applied evaluation of anattrition probability of a product held by a consumer, or may uniformlyinclude or exclude a consumer from a future product offering which maycompletely mismanage a consumer's needs and therefore may actually pushthe consumer out the door and into the arms of a waiting competitor.Furthermore, “the average Joe” approach is wholly inadequate forhierarchically ranking the consumers, and/or determining a clustering ofthe consumers.

The present disclosure provides for novel systems and computerizedmethods to be used by an organization (including, without limitation, abank or financial institution) to overcome the above-describeddeficiencies in the prior art. Embodiments of these methods include, forexample, viewing the clients in the database against all of the relevantproducts/services thereby capturing a more complete understanding of theclient/product environment allowing for directing improved interactionswith the clients. In certain embodiments, there are five main pointsthat may be taken into account: (1) each client may be viewed from twoperspectives, (a) the current product mix of products/services that theclient has/uses, and (b) one or more potential future product mix ofproducts/services; (2) each of the product mixes may be assigned avalue; (3) a difference between a current and a potential future productmix may lead to multiple product recommendations for a client versus thetraditional single product recommendation based on prior art models; (4)a matrix of current values versus a potential values may be determinedand analyzed to thereby direct future interactions with the client(s);and (5) a client's movement over time through the matrix may be trackedto thereby determine patterns applicable to that client. Embodimentsdescribed herein may be applied to a cluster or segment of clients whoare sufficiently alike to one another and dissimilar to otherclients/clusters/segments.

Accordingly, it is an object of the present disclosure to provide amethod for directing an interaction with at least a first consumerand/or evaluating a consumer database, where the method may includeproviding a computer database comprising first information about pluralconsumers and second information about predetermined products, whereinthe plural consumers include the first consumer, and wherein each of theplural consumers is associated with a current product mix comprisingcertain ones of the predetermined products independent of an associationof another consumer with the predetermined products. Additionally, themethod may calculate, using a computer processor, individually for eachone of the plural consumers, (i) an aggregate first Residual Life TimeValue (“RLTV”) estimate from the time variable products in the currentproduct mix for said one consumer; (ii) an aggregate second RLTVestimate from the finite duration products in the current product mixfor said one consumer; (iii) an aggregate third RLTV estimate from theaggregate first RLTV estimate and from the aggregate second RLTVestimate; and (iv) an aggregate PLTV estimate from preselected productsnot in the current product mix for said one consumer. Furthermore, themethod may: calculate, using a processor, the likelihood of the firstconsumer to acquire one or more of the predetermined products not in thecurrent product mix for the first consumer; analyze a distribution ofthe aggregate third RLTV estimates for the plural consumers; analyze adistribution of the aggregate PLTV estimates for the plural consumers;evaluate the first consumer as a function of the distribution of thethird aggregate RLTV estimates and as a function of the distribution ofthe aggregate PLTV estimates; and interact with the first consumer basedon said evaluation of the first consumer.

Additionally, the above method may further include stratifying thedatabase into plural segments according to a predetermined criteria, andwherein each of the plural consumers may be assigned to one of theplural segments according to the predetermined criteria.

Further, the above method may include determining a matrix of valuesfrom the distribution of the aggregate third RLTV estimates for theplural consumers and from the distribution of the aggregate PLTVestimates for the plural consumers; wherein the matrix may have N numberof rows encompassing a first range of quantities for the distribution ofthe aggregate third RLTV estimates and may have M number of columnsencompassing a second range of quantities for the distribution of theaggregate PLTV estimates thereby creating a matrix of X cells whereX=N*M (where M may be greater than, less than, or equal to N).

Still further, the above method may assign the first consumer to one ofthe X cells based at least in part on the evaluation of the firstconsumer.

Yet further, the above method may determine an interaction with thefirst consumer based at least in part on the cell assignment.

Even further, the above method may assign the first consumer to one ofthe X cells based at least in part on a recalculated aggregate thirdRLTV estimate and a recalculated aggregate PLTV estimate.

Even still further, the above method may determine the interaction withthe first consumer based at least in part on a difference between thecell assignment of the first consumer based at least in part on theaggregate third RLTV estimate for the first consumer and the aggregatePLTV estimate for the first consumer and the cell assignment of thefirst consumer based at least in part on the recalculated aggregatethird RLTV estimate and the recalculated aggregate PLTV estimate.

It is another object of the present disclosure to provide a system forevaluating a first consumer, including: a computer database comprisingfirst information about plural consumers and second information aboutpredetermined products, wherein the plural consumers include the firstconsumer, and wherein each of the plural consumers is associated with acurrent product mix comprising certain ones of the predeterminedproducts independent of an association of another consumer with thepredetermined products; a computer processor; and a computer readablestorage medium comprising computer-executable instructions storedthereon, said instructions when executed causing said processor to: (1)individually for each one of the plural consumers: (i) calculate anaggregate first Residual Life Time Value (“RLTV”) estimate from the timevariable products in the current product mix for said one consumer, (ii)calculate an aggregate second RLTV estimate from the finite durationproducts in the current product mix for said one consumer, (iii)calculate an aggregate third RLTV estimate from the aggregate first RLTVestimate and from the aggregate second RLTV estimate, and (iv) calculatean aggregate PLTV estimate from preselected products not in the currentproduct mix for said one consumer; and to (2) calculate the likelihoodof the first consumer to acquire one or more of the predeterminedproducts not in the current product mix for the first consumer; (3)analyze a distribution of the aggregate third RLTV estimates for theplural consumers; (4) analyze a distribution of the aggregate PLTVestimates for the plural consumers; and (5) evaluate the first consumeras a function of the distribution of the third aggregate RLTV estimatesand as a function of the distribution of the aggregate PLTV estimates.

It is yet another object of the present disclosure to provide a methodfor directing an interaction with at least a first consumer, the methodmay include providing a computer database which contains firstinformation about plural consumers and second information aboutpredetermined products, wherein the plural consumers include the firstconsumer, and wherein each of the plural consumers is associated with acurrent product mix comprising certain ones of the predeterminedproducts independent of an association of another consumer with thepredetermined products; and for a time variable product in the currentproduct mix for a one of the plural consumers: (i) determining abaseline product survival curve, (ii) determining a shift in thebaseline product survival curve as a function of characteristics of saidone consumer to thereby determine a consumer product survival curve,(iii) calculating, using a processor, an area under the consumer productsurvival curve, (iv) calculating, using a processor, an estimatedpotential residual profit from the calculated area, (v) determining afirst Residual Life Time Value (“RLTV”) estimate for said time variableproduct for said one consumer from the calculated estimated potentialresidual profit, (vi) repeating (i) through (v) for each time variableproduct in the current product mix for said one consumer, and (vii)determining an aggregate first RLTV estimate for said one consumer fromthe first RLTV estimate for each said time variable product for said oneconsumer; the foregoing may be repeated for each one of the pluralconsumers.

Additionally, the method may include, for a finite duration product inthe current product mix for a one of the plural consumers: (i)determining a remaining outstanding balance (as an example, since thismay apply to any monetary amount such as purchases made during a timeperiod, or a price), (ii) multiplying, using a processor, the remainingoutstanding balance by a funds transfer pricing value for said finiteduration product to thereby determine an approximate residual value,(iii) determining a second RLTV estimate for said finite durationproduct for said one consumer from the approximate residual value, (iv)repeating (i) through (iii) for each finite duration product in thecurrent product mix for said one consumer, and (v) determining anaggregate second RLTV estimate for said one consumer from the secondRLTV estimate for each said finite duration product for said oneconsumer; the foregoing may be repeated for each one of the pluralconsumers.

Further, the method may include, individually for each of the pluralconsumers, determining an aggregate third RLTV estimate from thatconsumer's aggregate first RLTV estimate and from that consumer'saggregate second RLTV estimate; and calculating, using a processor, thelikelihood of the first consumer to acquire one or more of thepredetermined products not in the current product mix for the firstconsumer.

Still further, the method may include, for a preselected product not inthe current product mix of a one of the plural consumers: (i)determining a baseline product survival curve, (ii) calculating, using aprocessor, an area under the baseline product survival curve, (iii)calculating, using a processor, an estimated potential residual profitfrom the calculated area, (iv) determining a Potential Life Time Value(“PLTV”) estimate for said preselected product for said one consumerfrom the calculated area, (v) repeating (i) through (iv) for eachpreselected product not in the current product mix for said oneconsumer, and (vi) determining an aggregate PLTV estimate for said oneconsumer from the PLTV estimate for each said preselected product forsaid one consumer; the foregoing may be repeated for each one of theplural consumers.

Yet further, the method may include analyzing a distribution of theaggregate third RLTV estimates for the plural consumers; analyzing adistribution of the aggregate PLTV estimates for the plural consumers;evaluating the first consumer as a function of the distribution of thethird aggregate RLTV estimates and as a function of the distribution ofthe aggregate PLTV estimates; and interacting with the first consumerbased on said evaluation of the first consumer.

The above advantages, as well as many other advantages, of the presentdisclosure will be readily apparent to one skilled in the art to whichthe disclosure pertains from a perusal of the claims, the appendeddrawings, and the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram for a method of evaluating/strategizinginteractions with a client base according to an embodiment of thedisclosure.

FIG. 2 is a system diagram for a system for evaluating/strategizinginteractions with a client base according to an embodiment of thedisclosure.

FIGS. 3A through 3E constitute a flow diagram of a detailed method ofevaluating/strategizing interactions with a client base according to anembodiment of the disclosure.

FIG. 4 is a depiction of an exemplary matrix of Residual Life Time Value(“RLTV”) estimates and Potential Life Time Value (“PLTV”) estimatesshowing quartiles with exemplary general descriptions of consumers' lifetime value according to an embodiment of the disclosure.

FIG. 5 is a depiction of an exemplary matrix of Residual Life Time Value(“RLTV”) estimates and Potential Life Time Value (“PLTV”) estimatesindicating exemplary interaction directions for one or more consumers'life time value according to an embodiment of the disclosure.

FIG. 6 is a depiction of an exemplary matrix of Residual Life Time Value(“RLTV”) estimates and Potential Life Time Value (“PLTV”) estimatesincluding an exemplary path through the matrix for a hypotheticalconsumer based on RLTV and PLTV estimates taken at different timesaccording to an embodiment of the disclosure.

FIG. 7 is a depiction of an exemplary matrix of Residual Life Time Value(“RLTV”) estimates and Potential Life Time Value (“PLTV”) estimatesincluding an exemplary distribution of consumers and showing typicalpoints of entry and typical points of attrition for one or moreconsumers according to an embodiment of the disclosure.

FIG. 8A is a depiction of an exemplary matrix of Residual Life TimeValue (“RLTV”) estimates and Potential Life Time Value (“PLTV”)estimates showing deciles including an exemplary path through the matrixfor a hypothetical consumer based on RLTV and PLTV estimates taken atdifferent times according to an embodiment of the disclosure.

FIG. 8B is a depiction of three exemplary matrices of Residual Life TimeValue (“RLTV”) estimates and Potential Life Time Value (“PLTV”)estimates each taken at a different time. The matrices show quartilesand have been expanded along the time axis to show a construction of anexemplary path through the matrix for a hypothetical consumer based onRLTV and PLTV estimates taken at different times according to anembodiment of the disclosure.

FIG. 9 is a graph showing an exemplary baseline product survival curveaccording to an embodiment of the disclosure.

FIG. 10 is a graph showing an exemplary baseline product survival curve,an exemplary first consumer product survival curve which is a rightshifted from the exemplary product survival curve, and an exemplarysecond consumer product survival curve which is left shifted from theexemplary product survival curve.

DETAILED DESCRIPTION

As described below, the present disclosure provides for novel systemsand methods for capturing a more complete understanding of theclient/product environment thereby allowing for directing improvedinteractions with the clients. Such inventive systems and methods wouldbe useful, for example, for an organization (including, withoutlimitation, a bank or financial institution) to direct or determine astrategy for sales, marketing, cross-selling, and/or retaining one ormore of the consumers. Additionally, the organization could make use ofthe inventive systems and methods in order to evaluate attritionprobability of a product held by a consumer, exclude a consumer from afuture product offering, hierarchically rank the organization'sconsumers, and/or determine a clustering of the consumers to assist inother analyses.

Embodiments discussed herein may be referred to as the Lifetime Value(“LTV”) model and the outputs of the LTV model may be used, forinstance, by a sales and/or marketing division within an organization tomore efficiently and effectively service current consumers by directingtheir consumer interaction efforts where returns will be maximized. TheLTV model may incorporate mathematical concepts, economic and behavioralinformation of consumers, demographic and/or socio-economic information,and, in some embodiments, sales and marketing concepts which may be usedto obtain a matrix of information about the consumers in anorganization's database and where a particular consumer may be placed inthat matrix. The output of the LTV model may be used to direct one ormore future interactions with a particular consumer or consumers to, forinstance, maximize that consumer's value to the organization as well asproviding products and services to the consumer to maximize thatconsumer's satisfaction and/or predict which products/services thatconsumer may most want or need. As a non-limiting example, the LTV modelmay be used to hierarchically rank a database of current consumers withrespect to the consumers' current value to the organization as well asthe consumers' future potential value to the organization. The LTV maymake use of a highly integrated system and method, as described herein,which includes various mathematically generated algorithms resulting ina series of consumer metrics previously unknown to the organization.These new metrics may be used strategically or tactically in directinginteractions with one or more of the consumers in the database. Thesemetrics include, but are not limited to: (a) a current lifetime value ofa particular consumer to the organization which may be based on thatparticular consumer's product/service portfolio as it currently exists(sometimes referred to herein as a “current product mix”; where thecurrent product mix may include products and/or services); (b) anexpected future lifetime value which may be based upon modeledexpectations that are a function of the currently heldproducts/services, demographics, and/or socio-economic factors; (c) acomposite lifetime value that may be used to hierarchically rankconsumers; (d) a “next three most likely service” enrollment hierarchyfor a consumer based on the output of the LTV model; (e) a clusteringmodel which enables the clustering of similarly-behaving consumers basedon the consumers' behaviors specific to their interactions with theorganization, and (f) a life cycle tracking capability to enable theorganization to track the life cycle of a given consumer or group ofconsumers across time. Such life cycle tracking provides importantinformation regarding interactions between the organization and itsconsumers including longer term future consumer value, cross-sell andup-sell opportunities, attrition abatement, as well as other factors.

While the present description of the inventive embodiments may bedirected towards a particular scenario for ease of explanation, such asa banking/financial transaction scenario, one of skill in the art willunderstand that the inventive embodiments are not so limited and haveapplication in other scenarios. For example, a current product mix thatan organization in the banking industry may be interested in may includea checking account (perhaps different types of checking accounts aretreated separately), savings account, and a credit card. A currentproduct mix that an organization in the consumer products industry maybe interested in may include a lawn mower, hand tools, power tools,ladders, garden upkeep service, and personal safety equipment.Additionally, while the description may in certain instances recite“products”, it will be understood by those of skill in the art that,where appropriate, “products” shall mean both products and services.

With attention directed towards FIG. 1 a flow diagram 100 is shown for amethod of directing/strategizing interactions with a client baseaccording to an embodiment of the disclosure. At step 101, a computerdatabase provided, such as the database shown in box 201A in FIG. 2. Thecomputer database may include information on each of the consumers thatcurrently have products and/or use services provided by an organizationas well as information about the particular products/services that arein the consumer's current product mix. The products/services mayinclude, but are not limited to, demand deposit account (“DDA”),personal savings account (“PSV”), credit card (“CCD”), direct loan,indirect loan, line of credit, certificate of deposit, individualretirement account, mortgage, wire transfer, investments, etc. Thedatabase may further include such information as: consumer productsowned/purchased; purchase, return, or expiration dates of theproducts/services; transaction data (e.g., if/when a return is made,if/when a deposit is made); other product/service data (e.g., balances,size, losses, term); channel data (e.g., store, website, service usage);consumer demographics (e.g., age, income, socio-economic data);firmagraphics (e.g., North American Industry Classification System code,sales size); purchased client demand estimates; segment categorizationsfor the consumers; revenue drivers (e.g., product price, margins, fees);cost drivers (e.g., variable product or service costs); attritionhistory and/or estimates on a product-by-product basis); consumercontact response data; consumer strategy data; and other informationthat may be useful to use in the LTV model.

The database may have previously been analyzed and statisticallyevaluated for particular products of interest at the product level, forexample, DDA, PSV, and CCD products. Additionally, the consumers in thedatabase may be stratified into multiple groups where each of the groupshave similar statistical characteristics. The product evaluation for aconsumer may include utilizing the entire sub-populations of aparticular group and may result in, for example, one or more of thefollowing statistical results: sample size, average of a series of timeunit (e.g., twelve month) averages, standard deviation, skewness,kurtosis, and percentiles (e.g., 25^(th) percentile, 50^(th) percentile(the median), 75^(th) percentile, 95^(th) percentile, etc.). Thestratification of the consumers into multiple consumer groups may beaccomplished for any desired number of groups, such as four groups, solong as the members of the groups have similar statisticalcharacteristics. These particular groups may include, for example, a“core client” group, a “top core client” group, an “affluent client”group, and an “ultra high net worth client” group. As a non-limitingexample, approximately 90% of the consumers in the database may belongto the “core client” group with diminishing percentages for the othergroups (e.g., approximately 5% for the “top core client” group;approximately 3% for the “affluent client” group, and approximately 2%for the “ultra high net worth client” group).

At step 102, a first consumer in the database may be selected. At step103, an aggregate first Residual Life Time Value (“RLTV”) estimate,referred to as “aggregate RLTV1” in step 103 of FIG. 1, may becalculated from the time variable products (each one separately) in thecurrent product mix for the first consumer chosen in step 102. Theprocessor 201B in FIG. 2 may be used for this calculation. Time variableproducts may include, for example, DDA, PSV, and CCD products. Forexample, if the first consumer only has DDA and CCD products, then onlyinformation related to those products would be used for determining theaggregate RLTV1. The calculation of RLTV estimates will be discussedfurther below with respect to FIG. 3.

At step 104, an aggregate second RLTV estimate, referred to as“aggregate RLTV2” in step 104 of FIG. 1, may be calculated from thefinite duration products (each one separately) in the current productmix for the first consumer chosen in step 102. The processor 201B inFIG. 2 may be used for this calculation. Finite duration products mayinclude, for example, direct loans and indirect loans. For example, ifthe first consumer only has an indirect loan, then only informationrelated to the indirect loan would be used for determining the aggregateRLTV2. The calculation of the RLTV estimates will be discussed furtherbelow with respect to FIG. 3.

At step 105, an aggregate third RLTV estimate, referred to as “aggregateRLTV3” in step 105 of FIG. 1, may be calculated from the aggregate RLTV1and aggregate RLTV2 estimates. The aggregate RLTV3 may be the sum of theaggregate RTLV1 and the aggregate RLTV2. The processor 201B in FIG. 2may be used for this calculation.

At step 106, an aggregate Potential Life Time Value (“PLTV”) estimate,referred to as an “aggregate PLTV” in step 106 of FIG. 1, may becalculated from preselected products that are not in the current productmix of the first consumer chosen in step 102. The processor 201B in FIG.2 may be used for this calculation. The preselected products may includeany product offered by the organization which the first consumer chosenin step 102 does not have in that consumer's current product mix. In apreferred embodiment, the preselected products may be a group ofproducts (e.g., “demand deposit account”) rather than individual typesof demand deposit accounts (e.g., interest-bearing accounts, freechecking, etc.). The calculation of the PLTV estimates will be discussedfurther below with respect to FIG. 3.

At step 107, a decision is made as to whether there are furtherconsumers in the database for whom the RLTV1, RLTV2, RLTV3, and PLTVestimates need to be calculated. If so, a second consumer is selectedand steps 103 through 106 are repeated for the pertinent information inthe database related to the second consumer. Steps 103-106 are loopedthrough in a similar manner for each consumer in the database. Once theRLTV1, RLTV2, RLTV3, and PLTV estimates are calculated for each consumerin the database, flow is directed to step 108.

At step 108, a distribution of the aggregate RLTV3 for each consumer inthe database is analyzed. At step 109, a distribution of the aggregatePLTV for each consumer in the database is analyzed. The processor 201Bin FIG. 2 may be used for these analyses. These analyses will bediscussed further with reference to FIGS. 4-8 below.

For step 110, simply for ease of explanation and without limiting thedisclosure in any way, the first consumer will be chosen for thisdiscussion. At step 110, the first consumer may be evaluated based onthe distribution of the aggregate RLTV3 s and the aggregate PLTVs. Theresult of this evaluation may be used to direct an interaction with thefirst consumer (or a cluster of consumers of which the first consumer isa member) as discussed herein.

Optionally, in some embodiments, step 111 may be included where a matrixmay be determined from the distribution of the aggregate RLTV3 s and theaggregate PLTVs. The matrix will be discussed further with reference toFIGS. 4-8 below.

In other embodiments, the matrix may be determined and the evaluation ofthe consumer may be based on the consumer's placement in the matrix. Forexample, after the analysis in step 109, a matrix may then be determinedfrom the distribution of the aggregate RLTV3 s and the aggregate PLTVs,as shown in step 112. Then, in step 113, the first consumer may beevaluated based on the placement of the first consumer in the matrix, asdiscussed further below in relation to FIGS. 4-8. The result of thisevaluation may be used to direct an interaction with the first consumer(or a cluster of consumers of which the first consumer is a member) asdiscussed herein.

With attention now directed toward FIG. 2, a system diagram 200 for asystem for strategizing interactions with a client base according to anembodiment of the disclosure is presented. Database 201A processor 201B,each as discussed above, are communicatively connected so as to exchangeinformation between the two devices. Computer readable storage medium201C is communicatively connected to the processor 201B. Blocks 202-211in the computer readable storage medium 201C are similar to steps102-111 in FIG. 1. Computer readable storage medium 201C has storedthereon computer-executable instructions which, when executed, cause theprocessor to: selecting a first consumer from the database at block 202;calculate an aggregate first Residual Life Time Value (“RLTV”) estimatefrom the time variable products in the current product mix for the firstconsumer at block 203; calculate an aggregate second RLTV estimate fromthe finite duration products in the current product mix for the firstconsumer at block 204; calculate an aggregate third RLTV estimate fromthe aggregate first RLTV estimate and from the aggregate second RLTVestimate at block 205; and calculate an aggregate PLTV estimate frompreselected products not in the current product mix for the firstconsumer at block 206. The instructions, when executed, also cause theprocessor to determine if there are further consumers in the databasefor whom the RLTV1, RLTV2, RLTV3, and PLTV estimates need to becalculated at block 207. If so, a second consumer is selected and blocks203 through 206 are repeated for the pertinent information in thedatabase related to the second consumer. Blocks 203-206 are loopedthrough in a similar manner for each consumer in the database. Once theRLTV1, RLTV2, RLTV3, and PLTV estimates are calculated for each consumerin the database, flow is directed to block 208. Additionally, theinstructions, when executed, cause the processor to: analyze adistribution of the aggregate third RLTV estimates for the pluralconsumers at block 208; analyze a distribution of the aggregate PLTVestimates for the plural consumers at block 209; and evaluate the firstconsumer as a function of the distribution of the third aggregate RLTVestimates and as a function of the distribution of the aggregate PLTVestimates at block 210. Optionally, in some embodiments, theinstructions, when executed, may determine a matrix may from thedistribution of the aggregate RLTV3 s and the aggregate PLTVs at block211.

In other embodiments, the matrix may be determined and the evaluation ofthe consumer may be based on the consumer's placement in the matrix. Forexample, after the analysis in step 209, a matrix may then be determinedfrom the distribution of the aggregate RLTV3 s and the aggregate PLTVs,as shown in step 212. Then, in step 213, the first consumer may beevaluated based on the placement of the first consumer in the matrix, asdiscussed further below in relation to FIGS. 4-8. The result of thisevaluation may be used to direct an interaction with the first consumer(or a cluster of consumers of which the first consumer is a member) asdiscussed herein.

Considering FIGS. 3A through 3E, these figures constitute a flow diagram300A through 300E of a detailed method of strategizing interactions witha client base according to an embodiment of the disclosure. RegardingFIG. 3A, at step 301 a database may be provided similar to the databasediscussed above for step 101 in FIG. 1. At step 302, a time variableproduct (as discussed above) may be selected. The time variable productis in a current product mix of a consumer in the database who, for thepurpose of ease of understanding this discussion, will be initiallyidentified as the first consumer. This identification in no way is meantto impose any limitations on the breadth of the disclosure.

At step 303, a baseline product survival curve, as is known in the artsuch as when using the Kaplan-Meier method, may be determined for theselected time variable product for the first consumer. In an embodiment,the equation for the baseline product survival curve, which is afunction of time, is determined as well as the time at which theprobability of survival to the next time period is equal to apredetermined value, such as 0.05 (i.e., there is a 1 in 20 probabilityof survival). An exemplary product survival curve is shown in graph 900as curve 901 in FIG. 9, where the x-axis is time and the y-axis isprobability of survival. The area under the curve 901 which is to theleft of time=t* (where t* is a predefined time and may be the presenttime) represents value of the product that has already been realized.The area under the curve 901 which is to the right of time=t* representsvalue of the product yet to be realized. In preferred embodiments, thearea under the curve to the right of time=t* is cut off at t_(end) wheret_(end) may be defined to be the point in time where the probability ofincremental survival reaches some predetermined value, such as 0.05.

Again with reference to FIG. 3A, at step 304, a consumer productsurvival curve may be determined for the selected time variable productfor the first consumer. The consumer product survival curve may bedetermined using the Cox Proportional Hazard modeling technique, as isknown in the art. This technique may be used to determine the product'ssurvival “expansion” or “contraction” relative to the baseline productsurvival curve. As is known in the art, the Cox Proportional Hazardmodeling technique is a regression method which takes into account theindividual consumer's unique set of demographic characteristics and/orsocio-economic characteristics. Thus, the consumer product survivalcurve is consumer-specific rather than a “one size fits all” approach.Graph 1000 in FIG. 10 is generally illustrative of the Cox ProportionalHazard modeling technique. An exemplary baseline product survival curve1001 is shown which may be shifted (arrow 1004) to the right(“expanded”) to form a first consumer product survival curve 1002. Asstated above, this expansion of the products survival is based at leastin part on the consumer's demographic and/or socio-economiccharacteristics. Similarly, also shown in graph 1000 is a shift (arrow1005) of the baseline product survival curve 1001 to the left(“contracted”) to form a second consumer product survival curve 1003. Ascan be seen from graph 1000, at time=t, the probability of survivalaccording to the baseline product survival curve is approximately 0.43,while the “expanded” consumer product survival curve 1002 has aprobability of survival of approximately 0.46 and the “contracted”consumer product survival curve 1003 has a probability of survival ofapproximately 0.32.

Returning attention to FIG. 3A, at step 305, the area under the consumerproduct survival curve for the selected time variable product for thefirst consumer may be calculated. Similarly as discussed above withrespect to FIG. 9, the area under the consumer product survival curve tobe calculated is between a predetermined t* and a predetermined t_(end).This area may be calculated by any known means, such as by use ofintegrals or using trapezoidal summation approximation, as is known inthe art. Calculating the area under the curve to the right of t*eliminates the financial gains delivered to the organization prior to t*(which may be the current point in time).

At step 306, an estimated potential residual profit may be calculated todetermine the RLTV estimate for the selected time variable product forthe first consumer (referred to as “RLTV1” in step 306). The potentialresidual profit may be calculated using values that may include: theresidual life time area under the consumer product survival curve (asdetermined in step 305); a client-specific average 12-month balance forthe product (or some other convenient average balance); an estimatedprofit per dollar deposited, for example; the consumer's age (which maybe an actuarial life expectancy); and a retail segment (e.g., astratification group) to which the first consumer belongs. Additionally,a net present value for the selected product using an appropriatediscount factor, as is known in the art, may be included in thecalculation.

At step 307, a determination is made as to whether there are additionaltime variable products in the current product mix for the firstconsumer. If so, then another time variable product is selected andsteps 303-306 are repeated for the newly-selected time variable product.Steps 303-306 are looped through in a similar manner for each timevariable product in the first consumer's current product mix. Once theRLTV1 estimates are calculated for each time variable product in thefirst consumer's current product mix, flow is directed to step 308.

At step 308, an aggregate RLTV1 for the first consumer is determined.The aggregate RLTV1 may be the summation of the RLTV1 estimatesdetermined in step 306 for each time variable product in the currentproduct mix for the first consumer.

At step 309, a decision is made as to whether there are furtherconsumers in the database for whom the aggregate RLTV1 estimates need tobe determined. If so, a second consumer is selected and steps 302through 308 are repeated (including, as necessary, loops for steps303-306) for the pertinent information in the database related to thesecond consumer. Steps 302-308 are looped through in a similar mannerfor each consumer in the database. Once the aggregate RLTV1 estimatesare determined for each consumer in the database, flow is directed tostep 310 in FIG. 3B.

Regarding FIG. 3B, at step 310, a finite duration product/service (asdiscussed above) may be selected. The finite duration product is in acurrent product mix of a consumer in the database who, for the purposeof ease of understanding this discussion, will be initially identifiedas the first consumer. This identification in no way is meant to imposeany limitations on the breadth of the disclosure.

At step 311, an outstanding balance for the selected finite durationproduct is determined. At step 312, an approximate residual value may bedetermined to thereby determine an RLTV estimate for the selected finiteduration product for the first consumer (referred to as “RLTV2” in step311). The RLTV2 estimate may be obtained, for example, by multiplyingthe outstanding balance (i.e., the current dollar obligation) by thatparticular product's FTP (i.e., funds transfer pricing) rate.

At step 313, a determination is made as to whether there are additionalfinite duration products in the current product mix for the firstconsumer. If so, then another finite duration product is selected andsteps 311-312 are repeated for the newly-selected finite durationproduct. Steps 311-312 are looped through in a similar manner for eachfinite duration product in the first consumer's current product mix.Once the RLTV2 estimates are calculated for each finite duration productin the first consumer's current product mix, flow is directed to step314.

At step 314, an aggregate RLTV2 for the first consumer is determined.The aggregate RLTV2 may be the summation of the RLTV2 estimatesdetermined in step 312 for each finite duration product in the currentproduct mix for the first consumer.

At step 315, a decision is made as to whether there are furtherconsumers in the database for whom the aggregate RLTV2 estimates need tobe determined If so, a second consumer is selected and steps 310 through314 are repeated (including, as necessary, loops for steps 311-312) forthe pertinent information in the database related to the secondconsumer. Steps 310-314 are looped through in a similar manner for eachconsumer in the database. Once the aggregate RLTV2 estimates aredetermined for each consumer in the database, flow is directed to step316 in FIG. 3C.

Referring now to FIG. 3C, at step 316, a consumer may be selected fromthe database. The selected consumer will be initially identified as thefirst consumer for the purpose of ease of understanding this discussion.This identification in no way is meant to impose any limitations on thebreadth of the disclosure.

At step 317, a third aggregate RLTV estimate for the first consumer maybe determined (referred to as “aggregate RLTV3” in step 317) from, forexample, the summation of the aggregate RLTV1 and the aggregate RLTV2estimates determined above for the first consumer.

At step 318, a decision is made as to whether there are furtherconsumers in the database for whom the aggregate RLTV3 estimates need tobe determined. If so, a second consumer is selected and steps 316through 317 are repeated for the pertinent information in the databaserelated to the second consumer. Steps 316-317 are looped through in asimilar manner for each consumer in the database. Once the aggregateRLTV3 estimates are determined for each consumer in the database, flowis directed to step 319 in FIG. 3D.

Regarding FIG. 3D, at step 319, a first consumer in the database isselected where again, as above, the nomenclature “first consumer” isused here for convenience only and imposes no limitations to the breadthof the disclosure. At step 320, a likelihood of the first consumer toacquire one or more products that are not in the current product mix ofthe first consumer is calculated. This calculation may entail utilizinglogistic regression methodologies, as are known in the art, and mayfurther entail conditional probabilities, as are known in the art, toascertain the likelihood of this specific client acquiring a specificproduct/service given this specific client's current product mix as wellas this specific client's demographic characteristics and/orsocio-economic characteristics.

At step 321, a product/service that is not in a current product mix ofthe first consumer is selected. At step 322, a baseline product survivalcurve, as discussed above, may be determined for the selected productfor the first consumer. At step 323, the area under the baseline productsurvival curve for the selected product for the first consumer may becalculated. The area is taken from the initial point of futureenrollment (which may be an estimate or an assumed time) through to thepredetermined terminal point (e.g., t_(end) in FIG. 9). At step 324, anestimated potential profit may be calculated to determine the PLTVestimate for the selected product for the first consumer (referred to as“PLTV” in step 324). The PLTV represents the expected lifetime value ofthe selected product.

At step 325, a determination is made as to whether there are additionalproducts not in the current product mix for the first consumer. If so,then another product not in the current product mix of the firstconsumer is selected and steps 322-324 are repeated for thenewly-selected product. Steps 322-324 are looped through in a similarmanner for each selected product that is not in the first consumer'scurrent product mix. Once the PLTV estimates are calculated for eachselected product not in the first consumer's current product mix, flowis directed to step 326.

At step 326, an aggregate PLTV for the first consumer is determined. Theaggregate PLTV may be the summation of the PLTV estimates determined instep 324 for each selected product not in the current product mix forthe first consumer.

At step 327, a decision is made as to whether there are furtherconsumers in the database for whom the aggregate PLTV estimates need tobe determined. If so, a second consumer is selected and steps 320through 326 are repeated (including, as necessary, loops for steps322-324) for the pertinent information in the database related to thesecond consumer. Steps 320-326 are looped through in a similar mannerfor each consumer in the database. Once the aggregate PLTV estimates aredetermined for each consumer in the database, flow is directed to step328 in FIG. 3E.

Referring now to FIG. 3E, at step 328, a distribution of the aggregateRLTV3 for each consumer in the database is analyzed. At step 329, adistribution of the aggregate PLTV for each consumer in the database isanalyzed. The processor 201B in FIG. 2 may be used for these analyses.These analyses will be discussed further with reference to FIGS. 4-8below.

For step 330, simply for ease of explanation and without limiting thedisclosure in any way, the first consumer will be chosen for thisdiscussion. At step 330, the first consumer is evaluated based on thedistribution of the aggregate RLTV3 s and the aggregate PLTVs. At step331, the result of the evaluation in step 330 may be used to direct aninteraction with the first consumer (or a cluster of consumers of whichthe first consumer is a member) as discussed herein and/or theevaluation may entail a hierarchical ranking of the consumers in thedatabase or the determination of a clustering of the consumers in thedatabase.

Optionally, in some embodiments, step 332 may be included where a matrixmay be determined from the distribution of the aggregate RLTV3 s and theaggregate PLTVs. The matrix will be discussed further with reference toFIGS. 4-8 below.

In other embodiments, the matrix may be determined and the evaluation ofthe consumer may be based on the consumer's placement in the matrix andthe result of that evaluation may be used to direct an interaction withthe consumer, as discussed above. For example, after the analysis instep 329, a matrix may then be determined from the distribution of theaggregate RLTV3 s and the aggregate PLTVs, as shown in step 333. Then,in step 334, the first consumer may be evaluated based on the placementof the first consumer in the matrix, as discussed further below inrelation to FIGS. 4-8. In step 335, the result of this evaluation may beused to direct an interaction with the first consumer (or a cluster ofconsumers of which the first consumer is a member).

With attention now directed to FIG. 4, the matrix 400 is a depiction ofan exemplary matrix of Residual Life Time Value (“RLTV”) estimates andPotential Life Time Value (“PLTV”) estimates showing quartiles withexemplary general descriptions of consumers' life time value accordingto an embodiment of the disclosure. As can be seen from the matrix 400,RLTV values increase along the horizontal axis and represent aconsumer's likely residual value on products/services current held. PLTVvalues increase along the vertical axis and represent a consumer'spotential lifetime value of products/services not yet held by theconsumer but have a cumulative likelihood of being acquired given theconsumer's current product mix of products/services. As discussed above,for example, with respect to FIGS. 1 (at step 111) and 3E (at step 332),the matrix may be determined from the distribution of the aggregateRLTV3 s and PLTVs for each of the consumers in the database. Theaggregate RLTV3 s may be divided into quartiles of statisticaldistribution, as shown in matrix 400 of FIG. 4. Likewise, the aggregatePLTVs may be divided into quartiles of statistical distribution asshown. It shall be understood by those of skill in the art that otherstatistical distribution schemes, such as deciles or other usefulquantile schemes, may be implemented instead of quartiles. In someembodiments, the evaluation of the consumer may be determined based onthe consumer's placement in the matrix.

The cluster represented by the block designated 401 in matrix 400represents a cluster with low current value (i.e., between the 0^(th)and 25^(th) percentile of RLTV values) and low potential value (i.e.,between the 0^(th) and 25^(th) percentile of PLTV values). Similarly,the cluster represented by the block designated 402 in matrix 400represents a cluster with low current value (i.e., between the 0^(th)and 25^(th) percentile of RLTV values) and high potential value (i.e.,between the 75^(th) and 100^(th) percentile of PLTV values). Likewise,the cluster represented by the block designated 403 in matrix 400represents a cluster with high current value (i.e., between the 75^(th)and 100^(th) percentile of RLTV values) and low potential value (i.e.,between the 0^(th) and 25^(th) percentile of PLTV values).Correspondingly, the cluster represented by the block designated 404 inmatrix 400 represents a cluster with high current value (i.e., betweenthe 75^(th) and 100^(th) percentile of RLTV values) and high potentialvalue (i.e., between the 75^(th) and 100^(th) percentile of PLTVvalues). Comparably, the cluster represented by the block designated 405in matrix 400 represents a cluster with moderately low current value(i.e., between the 25^(th) and 50^(th) percentile of RLTV values) andmoderately high potential value (i.e., between the 50^(th) (median) and75^(th) percentile of PLTV values). These clusters, as discussed belowwith respect to FIG. 5, may be useful in directing future interactionsby the organization with a consumer that is currently placed within aparticular cluster.

Referring now to FIG. 5, the matrix 500 is a depiction of an exemplarymatrix of Residual Life Time Value (“RLTV”) estimates and Potential LifeTime Value (“PLTV”) estimates indicating exemplary interactiondirections for one or more consumers' life time value according to anembodiment of the disclosure. The matrix 500 is divided into quartilessimilar to the matrix 400 in FIG. 4. Of course, the matrix 500 could bedivided into any useful quantile scheme. The individual clusters inmatrix 500 include exemplary strategies that may be employed by anorganization when interacting with a particular consumer or asimilarly-situated group of consumers. Exemplary strategies in certainembodiments may include: attrition abatement (“AA”); cross-sell (“CS”);unrestricted sales potential (“S”); up-sell (“US”); standard consumerservicing (“SRVC”); and optimize consumer servicing (“MAX SRVC”). Thoseof skill in the art will understand that the above-listed strategies arenot all-encompassing and that other strategies may be employed by anorganization. Furthermore, an organization may come to understand overtime that interactions with certain clusters of consumers may changeover time and the organization may replace a current strategy withanother, more effective, strategy.

For example, in the block designated 501 in matrix 500, which maycorrelate to block 401 in matrix 400, consumers with RLTV and PLTVvalues that place them in this cluster typically will have a low currentvalue and a low potential value. The organization's strategy for dealingwith consumers in block 501 may be “SRVC”, i.e., provide standardconsumer servicing. Therefore, for example, if the methodology in FIGS.3A-3E is followed and a matrix is determined, such as in step 332(which, as discussed above, may precede steps 330 and 331) and theevaluation of a particular consumer places that consumer in the matrixwithin block 501, the organization may then be directed to interactingwith that consumer by providing that consumer with standard consumerservicing.

Similarly, if the evaluation of a particular consumer places thatconsumer in the matrix within block 502 (which may correspond to lowcurrent value and high potential value as shown in block 402), theorganization may then be directed to interacting with that particularconsumer by taking an attrition abatement strategy (e.g., attempting toprevent the organization from losing the consumer) and/or assume anapproach of unrestricted selling to the consumer (e.g., to build thatconsumer's relationship/loyalty with the organization).

Likewise, if the evaluation of a particular consumer places thatconsumer in the matrix within block 503 (which may correspond to highcurrent value and low potential value as shown in block 403), theorganization may then be directed to interacting with that particularconsumer by taking a maximum service approach (e.g., optimizing consumerservicing to prevent the organization from losing the consumer) and/orassume an approach of up-selling to the consumer (e.g., attempting toreplace one or more of the consumer's current products/services withproducts/services of greater marginal profitability).

Correspondingly, if the evaluation of a particular consumer places thatconsumer in the matrix within block 504 (which may correspond to highcurrent value and high potential value as shown in block 404), theorganization may then be directed to interacting with that particularconsumer by taking an attrition abatement approach, and/or a cross-sellapproach (e.g., present/sell product/service complements), and/or anup-selling approach.

Comparably, if the evaluation of a particular consumer places thatconsumer in the matrix within block 505 (which may correspond tomoderately low current value and moderately high potential value asshown in block 405), the organization may then be directed tointeracting with that particular consumer by taking a cross-sellingapproach and/or adopting an unrestricted selling strategy.

With attention now directed towards FIG. 6, matrix 600 is a depiction ofan exemplary matrix of Residual Life Time Value (“RLTV”) estimates andPotential Life Time Value (“PLTV”) estimates including an exemplary path601 through the matrix for a hypothetical consumer based on RLTV andPLTV estimates taken at different times according to an embodiment ofthe disclosure. The details of the exemplary path 601, such as its size,shape, curvature, smoothness, slope, starting point, and ending point,are exemplary only and in no way limit the disclosed embodiments. Theexemplary path 601 represents one possible path a consumer and/orsimilarly-situated cluster of consumers may transition through thematrix. For a particular consumer represented by the exemplary path 601,the particular consumer entered the matrix 600 in the low current value,low potential value cluster, transitioned through the matrix over time,as discussed in more detail below with respect to FIG. 8B, and left thematrix from the moderately low current value, low potential valuecluster. Those of skill in the art will readily realize that there aremany other possible paths a consumer may take through the matrix or thatthe consumer may never stray from one, or a few, boxes in the matrix.The exemplary path 601 is developed over time as will be discussedfurther below with respect to FIG. 8B.

Focusing now on FIG. 7, matrix 700 is a depiction of an exemplary matrixof Residual Life Time Value (“RLTV”) estimates and Potential Life TimeValue (“PLTV”) estimates including an exemplary distribution ofconsumers and showing typical points of entry and typical points ofattrition for one or more consumers according to an embodiment of thedisclosure. As a non-limiting example, block 701 (which may correspondto block 501 and block 401) represents that 6.0% of the consumers in anorganization's database (or, 6.0% of consumers in a particularstratification of an organization's database, as discussed above) areclustered in block 701 and therefore, corresponding to block 402 inmatrix 400, currently have a low current value and a low potentialvalue. Similarly, block 702 represents that 8.2% of the consumers areclustered in this block and have a low current value and a highpotential value. Likewise, block 703 represents that 3.7% of theconsumers are clustered in this block and have a high current value anda low potential value. Correspondingly, block 704 represents that 10.0%of the consumers are clustered in this block and have a high currentvalue and a high potential value. Comparably, block 705 represents that5.2% of the consumers are clustered in this block and have a moderatelylow current value and a moderately high potential value.

Additionally, matrix 700 shows exemplary typical points of entry ofconsumers into the matrix and typical points of attrition of consumersout of the matrix. As is apparent to those of skill in the art, theseare typical entry/attrition points and in no way are consumers limitedto entering/exiting the matrix at these points. As an example, when aconsumer first purchases products/services from an organization, thatconsumer will typically have a low current value to the organizationsince the consumer typically will only purchase a few products/servicesfrom the organization. Some, or perhaps most, of these new consumers maybe in the 25^(th) to 75^(th) percentile of potential value and thereforethe primary points of entry into the matrix may be as indicated inmatrix 700. In certain embodiments, typical exemplary points ofattrition may be where a consumer has low or moderately low currentvalue and low potential value. A primary point of attrition may be wherea consumer has low current value and low potential value where asecondary point of attrition may be where a consumer has a moderatelylow current value and a low potential value. Naturally, attrition of aconsumer may occur regardless of where that consumer currently is in thematrix and may be independent of that consumer's previous path throughthe matrix.

Referring now to FIG. 8A, the matrix 800A is a depiction of an exemplarymatrix of Residual Life Time Value (“RLTV”) estimates and Potential LifeTime Value (“PLTV”) estimates showing deciles including an exemplarypath through the matrix for a hypothetical consumer based on RLTV andPLTV estimates taken at different times according to an embodiment ofthe disclosure. In this matrix, the clustering of the consumers is basedon deciles rather than quartiles. The path 801, as with the path 601 inmatrix 600, is developed over time and is one of a multitude of possiblepaths a consumer may take through the matrix.

Turning now to FIG. 8B, depiction 800B is a representation of threeexemplary matrices of Residual Life Time Value (“RLTV”) estimates andPotential Life Time Value (“PLTV”) estimates each taken at a differenttime. The matrices show quartiles and the matrices have been expandedalong the time axis to show a construction of an exemplary path throughthe matrix for a hypothetical consumer based on RLTV and PLTV estimatestaken at different times according to an embodiment of the disclosure.While for simplicity's sake only three separate matrices are shown,those of skill in the art will readily understand that many moreevaluations of the hypothetical consumer may be taken to further definethe exemplary path 802 through the matrix. Accordingly, the exemplarypath 802 through the matrix associated with time T₁ may be constructedfrom evaluating a consumer (or similarly-situated consumers) atdifferent points of time and tracking the consumer's evaluations atthose points of time. For example, at time T₁, the consumer may beevaluated to be at point 803. At time T₂, the consumer may again beevaluated and the results of this second evaluation may place theconsumer at the point 804. At time T₃, the consumer may yet again beevaluated and the results of this third evaluation may place theconsumer at the point 805. Collapsing these evaluations into a singlematrix may result in the path 802.

The times T₁, T₂, and T₃ may represent monthly, biweekly, quarterly, orany other convenient time interval. It is not necessary that the timeintervals between evaluations remain constant. As the organizationbuilds, for example, a monthly history of consumer clusteringattributes, the organization may then use this history (e.g., the path802) to estimate a dynamic model for consumer behaviors across time.Once this dynamic model of consumer migration through the matrix isestablished, the organization may then be able to customize further itsfuture interactions with consumers, specifically with consumerssimilarly situated. Thus, the methodology discussed herein may be usedto direct and/or refine an organization's future interaction with itsconsumers and/or evaluate the organization's consumer database.

While preferred embodiments of the present disclosure have beendescribed, it is to be understood that the embodiments described areillustrative only and that the scope of the invention is to be definedsolely by the appended claims when accorded a full range of equivalents,many variations and modifications naturally occurring to those of skillin the art from a perusal hereof.

We claim:
 1. A system for determining an interaction strategy with atleast a first consumer, comprising: a computer database comprising firstinformation about plural consumers and second information aboutpredetermined products, wherein the plural consumers include the firstconsumer, and wherein each of the plural consumers is associated with acurrent product mix comprising certain ones of the predeterminedproducts independent of an association of another consumer with thepredetermined products; a computer processor; and a computer readablestorage medium comprising computer-executable instructions storedthereon, said instructions when executed causing said processor to: (a)for a time variable product in the current product mix for a one of theplural consumers: (i) determine a baseline product survival curve; (ii)determine a shift in the baseline product survival curve as a functionof characteristics of said one consumer to thereby determine a consumerproduct survival curve; (iii) calculate an area under the consumerproduct survival curve; (iv) calculate an estimated potential residualprofit from the calculated area to thereby determine a first ResidualLife Time Value (“RLTV”) estimate for said time variable product forsaid one consumer; (v) repeat (a)(i) through (a)(iv) for each timevariable product in the current product mix for said one consumer; and(vi) determine an aggregate first RLTV estimate for said one consumerfrom the first RLTV estimate for each said time variable product forsaid one consumer; (b) repeat (a) for each one of the plural consumers;(c) for a finite duration product in the current product mix for a oneof the plural consumers: (i) determine a remaining outstanding balance;(ii) multiply the remaining outstanding balance by a funds transferpricing value for said finite duration product to determine anapproximate residual value to thereby determine a second RLTV estimatefor said finite duration product for said one consumer; (iii) repeat(c)(i) through (c)(ii) for each finite duration product in the currentproduct mix for said one consumer; and (iv) determine an aggregatesecond RLTV estimate for said one consumer from the second RLTV estimatefor each said finite duration product for said one consumer; (d) repeat(c) for each one of the plural consumers; (e) individually for each ofthe plural consumers, determine an aggregate third RLTV estimate fromthat consumer's aggregate first RLTV estimate and from that consumer'saggregate second RLTV estimate; (f) calculate the likelihood of a one ofthe plural consumers to acquire one or more of the predeterminedproducts not in the current product mix for said one consumer; (g) for apreselected product not in the current product mix of a one of theplural consumers: (i) determine a baseline product survival curve; (ii)calculate an area under the baseline product survival curve; (iii)calculate an estimated potential residual profit from the calculatedarea to thereby determine a Potential Life Time Value (“PLTV”) estimatefor said preselected product for said one consumer; (iv) repeat (g)(i)through (g)(iii) for each preselected product not in the current productmix for said one consumer; and (v) determine an aggregate PLTV estimatefor said one consumer from the PLTV estimate for each said preselectedproduct for said one consumer; (h) repeat (f) and (g) for each one ofthe plural consumers; (i) analyze a distribution of the aggregate thirdRLTV estimates for the plural consumers; (j) analyze a distribution ofthe aggregate PLTV estimates for the plural consumers; and (k) evaluatethe first consumer as a function of the distribution of the thirdaggregate RLTV estimates and as a function of the distribution of theaggregate PLTV estimates, wherein the evaluation results in adetermination of an interaction strategy with the first consumer.
 2. Thesystem of claim 1 wherein said computer database is stratified intoplural segments according to a predetermined criteria, and wherein eachof the plural consumers is assigned to one of the plural segmentsaccording to the predetermined criteria.
 3. The system of claim 2wherein the predetermined criteria comprises socio-economic informationfor each of the plural consumers.
 4. The system of claim 3 wherein thesocio-economic information comprises at least one of an income value andan age value.
 5. The system of claim 2 wherein the predeterminedcriteria comprises historic economic behavior for each of the pluralconsumers.
 6. The system of claim 1 wherein the time variable productscomprise predetermined products that do not have a predefinedtermination point.
 7. The system of claim 1 wherein the determination ofthe baseline product survival curve in (a)(i) includes evaluating thesecond information about said predetermined product for ones of theplural consumers associated with said predetermined product.
 8. Thesystem of claim 1 wherein the characteristics of the first consumerinclude socio-economic information.
 9. The system of claim 1 wherein thefinite duration products comprise predetermined products that have apredefined termination point.
 10. The system of claim 1 wherein thedetermination of a remaining outstanding balance in (c)(i) includesdetermining a remaining tenure.
 11. The system of claim 1 wherein thecalculation of the likelihood of the first consumer to acquire one ormore of the predetermined products not in the current product mix forthe first consumer comprises using a discrete choice regression method.12. The system of claim 11 wherein the calculation of the likelihood ofthe first consumer to acquire one or more of the predetermined productsnot in the current product mix for the first consumer further comprisesusing a conditional probability analysis.
 13. The system of claim 1wherein said computer readable storage medium further comprisescomputer-executable instructions stored thereon which, when executed,cause said processor to: (l) determine a matrix of values from thedistribution of the aggregate third RLTV estimates for the pluralconsumers and from the distribution of the aggregate PLTV estimates forthe plural consumers, wherein the determination of the matrix of valuesoccurs prior to the determination of an interaction strategy with thefirst consumer.
 14. The system of claim 13 wherein the matrix comprisesN number of rows encompassing a first range of quantities for thedistribution of the aggregate third RLTV estimates and M number ofcolumns encompassing a second range of quantities for the distributionof the aggregate PLTV estimates thereby creating a matrix of X cellswhere X=N*M.
 15. The system of claim 14 where M does not equal N. 16.The system of claim 14 wherein the first consumer is assigned to one ofthe X cells based at least in part on the evaluation of the firstconsumer.
 17. The system of claim 16 wherein the determination of aninteraction strategy with the first consumer is based at least in parton the cell assignment.
 18. The system of claim 16 wherein the aggregatethird RLTV estimate for the first consumer and the aggregate PLTVestimate for the first consumer are calculated at a first predeterminedtime and wherein the aggregate third RLTV estimate for the firstconsumer and the aggregate PLTV estimate for the first consumer arerecalculated at a second predetermined time.
 19. The system of claim 18wherein the first consumer is assigned to one of the X cells based atleast in part on the recalculated aggregate third RLTV estimate and therecalculated aggregate PLTV estimate.
 20. The system of claim 19 whereinthe determination of an interaction strategy with the first consumer isbased at least in part on a difference between the cell assignment ofthe first consumer based at least in part on the aggregate third RLTVestimate for the first consumer and the aggregate PLTV estimate for thefirst consumer and the cell assignment of the first consumer based atleast in part on the recalculated aggregate third RLTV estimate and therecalculated aggregate PLTV estimate.
 21. The system of claim 1 whereinthe first consumer comprises a cluster of consumers wherein each memberof the cluster meets a predetermined criteria for inclusion in thecluster.
 22. The system of claim 1 wherein the predetermined productscomprise predetermined products and predetermined services.
 23. Thesystem of claim 1 wherein the current product mix includes predeterminedproducts acquired by the consumer within a predetermined time frame. 24.The system of claim 1 wherein the current product mix includes servicesused by the consumer within a predetermined time frame.
 25. The systemof claim 1 wherein the determination of an interaction strategy with thefirst consumer comprises a determination of a sales strategy tailoredfor the first consumer.
 26. The system of claim 1 wherein thedetermination of an interaction strategy with the first consumercomprises a determination of a cross-selling strategy for the firstconsumer.
 27. The system of claim 1 wherein the determination of aninteraction strategy with the first consumer comprises a determinationof a marketing strategy tailored for the first consumer.
 28. The systemof claim 1 wherein the determination of an interaction strategy with thefirst consumer comprises a determination of a strategy for retaining thefirst consumer.